descriptor1 = scanContextDescriptor(ptCloud1); descriptor2 = scanContextDescriptor(ptCloud2); descriptor30 = scanContextDescriptor(ptCloud30); Compute the descriptor distance between the 1st and 2nd scan context descriptors, and between the 1st and 30th scan context descriptors. ...
Scan Context这个算法其实一开始是由Shape Context [2] 所启发的,而Shape Context是把点云的 local Keypoint 附近的点云形状 encode 进一个图像中。 Scan Context的不同在于,它不仅仅是count the number of points,而是采用了 maximum height of points in each bin(简单来说,就是取每一个bin中的所有point的z...
论文:Kim, Giseop, and Ayoung Kim. "Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map."2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. 开源工程:https://github.com/irapkaist/scancontext 目前已经集成在了一些开源...
在3.2节中我们提到的公式(6)进行最短距离计算时,要先找到最佳旋转n ∗,计算量很大,所以在本文中提出了一种"Two-phase Search",并提出了Ring key这个Descriptor(描述子)来进行匹配搜索: Ring key is a rotation-invariant descriptor, which is extracted from a scan context. Each row of a scan context, ...
在3.2节中我们提到的公式(6)进行最短距离计算时,要先找到最佳旋转 n∗ ,计算量很大,所以在本文中提出了一种"Two-phase Search",并提出了Ring key这个Descriptor(描述子)来进行匹配搜索: Ring key is a rotation-invariant descriptor, which is extracted from a scan context. Each row of a scan context,...
在3.2节中我们提到的公式(6)进行最短距离计算时,要先找到最佳旋转n∗ ,计算量很大,所以在本文中提出了一种"Two-phase Search",并提出了Ring key这个Descriptor(描述子)来进行匹配搜索:Ring key is a rotation-invariant descriptor, which is extracted from a scan context. Each row of a scan context, r...
在3.2节中我们提到的公式(6)进行最短距离计算时,要先找到最佳旋转 n∗ ,计算量很大,所以在本文中提出了一种"Two-phase Search",并提出了Ring key这个Descriptor(描述子)来进行匹配搜索: Ring key is a rotation-invariant descriptor,...
在3.2节中我们提到的公式(6)进行最短距离计算时,要先找到最佳旋转 n∗ ,计算量很大,所以在本文中提出了一种"Two-phase Search",并提出了Ring key这个Descriptor(描述子)来进行匹配搜索: Ring key is a rotation-invariant descriptor, which is extracted from a scan context. Each row of a scan context,...
一、构建Scan Context 二、计算Scan Context的每一行的均值作为RingKey 三、将所有历史帧的RingKey构建Kd-tree查找候选关键帧 四、遍历候选关键帧,选择距离最近的帧作为匹配帧。 计算距离的步骤 五、再用icp匹配 优缺点 优点 缺点 打赏 支付宝 微信 Scan Context: Egocentric Spatial Descriptor for Place Recognition...
Scan Context is a global descriptor for LiDAR point cloud, which is especially designed for a sparse and noisy point cloud acquired in outdoor environment. It encodes egocentric visible information as below: A user can vary the resolution of a Scan Context. Below is the example of Scan Contex...